The Effects of Race/Ethnicity, Age, and Area Deprivation Index (ADI) on COVID-19 Disease Early Dynamics: Washington, D.C. Case Study

The COVID-19 pandemic and its associated mitigation strategies have significant psychosocial, behavioral, socioeconomic, and health impacts, particularly in vulnerable US populations. Different factors have been identified as influencers of the transmission rate; however, the effects of area deprivation index (as a measure of social determinants of health, SDoH) as a factor on COVID-19 disease early dynamics have not been established. We determined the effects of area deprivation index (ADI) and demographic factors on COVID-19 outcomes in Washington, D.C. This retrospective study used publicly available data on COVID-19 cases and mortality of Washington, D.C., during March 31st–July 4th, 2020. The main predictors included area deprivation index (ADI), age, and race/ethnicity. The ADI of each census block groups in D.C. (n=433) were obtained from Neighborhood Atlas map. Using a machine learning-based algorithm, the outcome variables were partitioned into time intervals: time duration (P(i), days), rate of change coefficient (E(i)), and time segment load (P(i)×E(i)) for transmission rate and mortality. Correlation analysis and multiple linear regression models were used to determine associations between predictors and outcome variables. COVID-19 early transmission rate (E(1)) was highly correlated with ADI (SDoH; r= 0.88, p=0.0044) of the Washington, D.C. community. We also found positive association between ADI, age (0–17 years, r=0.91, p=0.0019), and race (African American/Black, r=0.86; p=0.0068) and COVID-19 outcomes. There was high variability in early transmission across the geographic regions (i.e., wards) of Washington, D.C., and this variability was driven by race/ethnic composition and ADI. Understanding the association of COVID-19 disease early transmission and mortality dynamics and key socio-demographic risk factors such as age, race, and ADI, as a measure of social determinants, will contribute to health equity/equality and distribution of economic resources/assistance and is essential for future predictive modeling of the COVID-19 pandemic to limit morbidity and mortality. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40615-022-01238-1.